Diabetes Prediction using Machine Learning Algorithm and Model Explained with LIME

Import Necessary files

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set()

from mlxtend.plotting import plot_decision_regions
import missingno as msno
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

from sklearn.metrics import confusion_matrix
from sklearn import metrics
from sklearn.metrics import classification_report
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline

Load Dataset

diabetes_df = pd.read_csv('/content/drive/MyDrive/ML-Learning/Datasets/diabetes.csv')
diabetes_df.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

diabetes_df.shape

(768, 9)

diabetes_df.isnull().any()

Pregnancies                 False
Glucose                     False
BloodPressure               False
SkinThickness               False
Insulin                     False
BMI                         False
DiabetesPedigreeFunction    False
Age                         False
Outcome                     False
dtype: bool

sns.pairplot(diabetes_df,hue='Outcome')
plt.show()
correlation=diabetes_df.corr()
correlation

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
Pregnancies	1.000000	0.129459	0.141282	-0.081672	-0.073535	0.017683	-0.033523	0.544341	0.221898
Glucose	0.129459	1.000000	0.152590	0.057328	0.331357	0.221071	0.137337	0.263514	0.466581
BloodPressure	0.141282	0.152590	1.000000	0.207371	0.088933	0.281805	0.041265	0.239528	0.065068
SkinThickness	-0.081672	0.057328	0.207371	1.000000	0.436783	0.392573	0.183928	-0.113970	0.074752
Insulin	-0.073535	0.331357	0.088933	0.436783	1.000000	0.197859	0.185071	-0.042163	0.130548
BMI	0.017683	0.221071	0.281805	0.392573	0.197859	1.000000	0.140647	0.036242	0.292695
DiabetesPedigreeFunction	-0.033523	0.137337	0.041265	0.183928	0.185071	0.140647	1.000000	0.033561	0.173844
Age	0.544341	0.263514	0.239528	-0.113970	-0.042163	0.036242	0.033561	1.000000	0.238356
Outcome	0.221898	0.466581	0.065068	0.074752	0.130548	0.292695	0.173844	0.238356	1.000000

Pair plot is used to understand the best set of features to explain a relationship between two variables or to form the most separated clusters. It also helps to form some simple classification models by drawing some simple lines or make linear separation in our dataset.

Exploratory Data Analysis (EDA)

diabetes_df.columns

Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],
      dtype='object')

diabetes_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB

diabetes_df.describe()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
count	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000	768.000000
mean	3.845052	120.894531	69.105469	20.536458	79.799479	31.992578	0.471876	33.240885	0.348958
std	3.369578	31.972618	19.355807	15.952218	115.244002	7.884160	0.331329	11.760232	0.476951
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.078000	21.000000	0.000000
25%	1.000000	99.000000	62.000000	0.000000	0.000000	27.300000	0.243750	24.000000	0.000000
50%	3.000000	117.000000	72.000000	23.000000	30.500000	32.000000	0.372500	29.000000	0.000000
75%	6.000000	140.250000	80.000000	32.000000	127.250000	36.600000	0.626250	41.000000	1.000000
max	17.000000	199.000000	122.000000	99.000000	846.000000	67.100000	2.420000	81.000000	1.000000

diabetes_df.describe().T

	count	mean	std	min	25%	50%	75%	max
Pregnancies	768.0	3.845052	3.369578	0.000	1.00000	3.0000	6.00000	17.00
Glucose	768.0	120.894531	31.972618	0.000	99.00000	117.0000	140.25000	199.00
BloodPressure	768.0	69.105469	19.355807	0.000	62.00000	72.0000	80.00000	122.00
SkinThickness	768.0	20.536458	15.952218	0.000	0.00000	23.0000	32.00000	99.00
Insulin	768.0	79.799479	115.244002	0.000	0.00000	30.5000	127.25000	846.00
BMI	768.0	31.992578	7.884160	0.000	27.30000	32.0000	36.60000	67.10
DiabetesPedigreeFunction	768.0	0.471876	0.331329	0.078	0.24375	0.3725	0.62625	2.42
Age	768.0	33.240885	11.760232	21.000	24.00000	29.0000	41.00000	81.00
Outcome	768.0	0.348958	0.476951	0.000	0.00000	0.0000	1.00000	1.00

diabetes_df.isnull().head(10)

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False	False
5	False	False	False	False	False	False	False	False	False
6	False	False	False	False	False	False	False	False	False
7	False	False	False	False	False	False	False	False	False
8	False	False	False	False	False	False	False	False	False
9	False	False	False	False	False	False	False	False	False

diabetes_df.isnull().sum()

Pregnancies                 0
Glucose                     0
BloodPressure               0
SkinThickness               0
Insulin                     0
BMI                         0
DiabetesPedigreeFunction    0
Age                         0
Outcome                     0
dtype: int64

This particular dataset all the missing values were given the 0 as a value which is not good for the authenticity of the dataset. Hence we will first replace the 0 value with the NAN value.

diabetes_df_copy = diabetes_df.copy(deep = True)

diabetes_df_copy[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']] = diabetes_df_copy[['Glucose','BloodPressure','SkinThickness','Insulin','BMI']].replace(0,np.NaN)

print(diabetes_df_copy.isnull().sum())

Pregnancies                   0
Glucose                       5
BloodPressure                35
SkinThickness               227
Insulin                     374
BMI                          11
DiabetesPedigreeFunction      0
Age                           0
Outcome                       0
dtype: int64

p = diabetes_df.hist(figsize = (20,20))

we have seen the distribution of each features whether it is dependent data or independent data and one thing which could always strike that why do we need to see the distribution of data? So the answer is simple it is the best way to start the analysis of the dataset as it shows the occurrence of every kind of value in the graphical structure which in turn lets us know the range of the data.

diabetes_df_copy['Glucose'].fillna(diabetes_df_copy['Glucose'].mean(), inplace = True)

diabetes_df_copy['BloodPressure'].fillna(diabetes_df_copy['BloodPressure'].mean(), inplace = True)
diabetes_df_copy['SkinThickness'].fillna(diabetes_df_copy['SkinThickness'].median(), inplace = True)

diabetes_df_copy['Insulin'].fillna(diabetes_df_copy['Insulin'].median(), inplace = True)
diabetes_df_copy['BMI'].fillna(diabetes_df_copy['BMI'].median(), inplace = True)

p = diabetes_df_copy.hist(figsize = (20,20))

p = msno.bar(diabetes_df)

import matplotlib.pyplot as plt

# Define the color wheel with a default color for missing values
color_wheel = {1: "#0392cf", 2: "#7bc043"}

# Map colors to outcomes
colors = diabetes_df["Outcome"].map(lambda x: color_wheel.get(x, "gray"))

# Plot the bar chart with colors
p = diabetes_df["Outcome"].value_counts().plot(kind="bar", color=colors)

# Show the plot
plt.show()

Above visualization it is clearly visible that our dataset is completely imbalanced in fact the number of patients who are diabetic is half of the patients who are non-diabetic.

plt.subplot(121)
sns.distplot(diabetes_df['Insulin'])
plt.subplot(122)
diabetes_df['Insulin'].plot.box(figsize=(16,5))
plt.show()

That’s how Distplot can be helpful where one will able to see the distribution of the data as well as with the help of boxplot one can see the outliers in that column and other information too which can be derived by the box and whiskers plot.

plt.figure(figsize=(12,10))
p = sns.heatmap(diabetes_df.corr(),annot=True,cmap ='RdYlGn')

diabetes_df_copy.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148.0	72.0	35.0	125.0	33.6	0.627	50	1
1	1	85.0	66.0	29.0	125.0	26.6	0.351	31	0
2	8	183.0	64.0	29.0	125.0	23.3	0.672	32	1
3	1	89.0	66.0	23.0	94.0	28.1	0.167	21	0
4	0	137.0	40.0	35.0	168.0	43.1	2.288	33	1

Scale the data

sc_X = StandardScaler()
X = pd.DataFrame(sc_X.fit_transform(diabetes_df_copy.drop(["Outcome"],axis = 1),), columns=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'])
X.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age
0	0.639947	0.865108	-0.033518	0.670643	-0.181541	0.166619	0.468492	1.425995
1	-0.844885	-1.206162	-0.529859	-0.012301	-0.181541	-0.852200	-0.365061	-0.190672
2	1.233880	2.015813	-0.695306	-0.012301	-0.181541	-1.332500	0.604397	-0.105584
3	-0.844885	-1.074652	-0.529859	-0.695245	-0.540642	-0.633881	-0.920763	-1.041549
4	-1.141852	0.503458	-2.680669	0.670643	0.316566	1.549303	5.484909	-0.020496

diabetes_df_copy.Outcome

0      1
1      0
2      1
3      0
4      1
      ..
763    0
764    0
765    0
766    1
767    0
Name: Outcome, Length: 768, dtype: int64

Model Building

X = diabetes_df.drop('Outcome', axis=1)
y = diabetes_df['Outcome']

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.33, random_state=7)

Random Forest

from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(max_features=2, n_estimators =200 ,bootstrap = True)
rfc.fit(X_train, y_train)

RandomForestClassifier(max_features=2, n_estimators=200)

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rfc_train = rfc.predict(X_train)

from sklearn import metrics
predictions = rfc.predict(X_test)
print("Accuracy_Score =", format(metrics.accuracy_score(y_test, predictions)))

Accuracy_Score = 0.7716535433070866

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test,predictions))

[[136  26]
 [ 32  60]]
              precision    recall  f1-score   support

           0       0.81      0.84      0.82       162
           1       0.70      0.65      0.67        92

    accuracy                           0.77       254
   macro avg       0.75      0.75      0.75       254
weighted avg       0.77      0.77      0.77       254

Decision Tree

from sklearn.tree import DecisionTreeClassifier
dtree = DecisionTreeClassifier()
dtree.fit(X_train, y_train)

DecisionTreeClassifier()

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from sklearn import metrics
predictions_1 = dtree.predict(X_test)
print("Accuracy Score =", format(metrics.accuracy_score(y_test,predictions_1)))

Accuracy Score = 0.7007874015748031

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, predictions_1))
print(classification_report(y_test,predictions_1))

[[127  35]
 [ 41  51]]
              precision    recall  f1-score   support

           0       0.76      0.78      0.77       162
           1       0.59      0.55      0.57        92

    accuracy                           0.70       254
   macro avg       0.67      0.67      0.67       254
weighted avg       0.70      0.70      0.70       254

XgBoost classifier

from xgboost import XGBClassifier
xgb_model = XGBClassifier(gamma=0)
xgb_model.fit(X_train, y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=0, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=None, n_jobs=None,
              num_parallel_tree=None, random_state=None, ...)

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from sklearn import metrics
xgb_pred = xgb_model.predict(X_test)
print("Accuracy Score =", format(metrics.accuracy_score(y_test, xgb_pred)))

Accuracy Score = 0.7283464566929134

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, xgb_pred))
print(classification_report(y_test,xgb_pred))

[[128  34]
 [ 35  57]]
              precision    recall  f1-score   support

           0       0.79      0.79      0.79       162
           1       0.63      0.62      0.62        92

    accuracy                           0.73       254
   macro avg       0.71      0.70      0.71       254
weighted avg       0.73      0.73      0.73       254

Support Vector Machine (SVM)

from sklearn.svm import SVC
svc_model = SVC()
svc_model.fit(X_train, y_train)

SVC()

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svc_pred = svc_model.predict(X_test)

from sklearn import metrics
print("Accuracy Score =", format(metrics.accuracy_score(y_test, svc_pred)))

Accuracy Score = 0.7480314960629921

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, svc_pred))
print(classification_report(y_test,svc_pred))

[[145  17]
 [ 47  45]]
              precision    recall  f1-score   support

           0       0.76      0.90      0.82       162
           1       0.73      0.49      0.58        92

    accuracy                           0.75       254
   macro avg       0.74      0.69      0.70       254
weighted avg       0.74      0.75      0.73       254

Logistic Regression

from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(solver = 'liblinear')
lr.fit(X_train, y_train)

LogisticRegression(solver='liblinear')

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lr_pred = lr.predict(X_test)

from sklearn import metrics
print("Accuracy Score =", format(metrics.accuracy_score(y_test, lr_pred)))

Accuracy Score = 0.7559055118110236

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, lr_pred))
print(classification_report(y_test,lr_pred))

[[141  21]
 [ 41  51]]
              precision    recall  f1-score   support

           0       0.77      0.87      0.82       162
           1       0.71      0.55      0.62        92

    accuracy                           0.76       254
   macro avg       0.74      0.71      0.72       254
weighted avg       0.75      0.76      0.75       254

KNN

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 9)
knn.fit(X_train, y_train)

KNeighborsClassifier(n_neighbors=9)

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knn_pred = knn.predict(X_test)

from sklearn import metrics
print("Accuracy Score =", format(metrics.accuracy_score(y_test, knn_pred)))

Accuracy Score = 0.7165354330708661

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, knn_pred))
print(classification_report(y_test,knn_pred))

[[132  30]
 [ 42  50]]
              precision    recall  f1-score   support

           0       0.76      0.81      0.79       162
           1       0.62      0.54      0.58        92

    accuracy                           0.72       254
   macro avg       0.69      0.68      0.68       254
weighted avg       0.71      0.72      0.71       254

AdaBoost

from sklearn.ensemble import AdaBoostClassifier
ada = AdaBoostClassifier(n_estimators=50,
                         learning_rate=1)
# Train Adaboost Classifer
model = ada.fit(X_train, y_train)

ada_pred = ada.predict(X_test)

from sklearn import metrics
print("Accuracy Score =", format(metrics.accuracy_score(y_test, ada_pred)))

Accuracy Score = 0.7244094488188977

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, ada_pred))
print(classification_report(y_test,ada_pred))

[[129  33]
 [ 37  55]]
              precision    recall  f1-score   support

           0       0.78      0.80      0.79       162
           1       0.62      0.60      0.61        92

    accuracy                           0.72       254
   macro avg       0.70      0.70      0.70       254
weighted avg       0.72      0.72      0.72       254

SVC as a base estimator

svc=SVC(probability=True, kernel='linear')

# Create adaboost classifer object
ada_svm =AdaBoostClassifier(n_estimators=50, base_estimator=svc,learning_rate=1)
model = ada_svm.fit(X_train, y_train)

ada_svm_pred = ada_svm.predict(X_test)

from sklearn import metrics
print("Accuracy Score =", format(metrics.accuracy_score(y_test, ada_svm_pred)))

Accuracy Score = 0.6377952755905512

from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, ada_svm_pred))
print(classification_report(y_test,ada_svm_pred))

[[162   0]
 [ 92   0]]
              precision    recall  f1-score   support

           0       0.64      1.00      0.78       162
           1       0.00      0.00      0.00        92

    accuracy                           0.64       254
   macro avg       0.32      0.50      0.39       254
weighted avg       0.41      0.64      0.50       254

Results of All Models

print("Accuracy_Score : Random Forest =", format(metrics.accuracy_score(y_test, predictions)))
print("Accuracy_Score : Decision Tree =", format(metrics.accuracy_score(y_test, predictions_1)))
print("Accuracy_Score : XgBoost classifier =", format(metrics.accuracy_score(y_test, xgb_pred)))
print("Accuracy_Score : Support Vector Machine (SVM) =", format(metrics.accuracy_score(y_test, svc_pred)))
print("Accuracy_Score : Logistic Regression =", format(metrics.accuracy_score(y_test, lr_pred)))
print("Accuracy_Score : KNN =", format(metrics.accuracy_score(y_test, knn_pred)))
print("Accuracy_Score : AdaBoost =", format(metrics.accuracy_score(y_test, ada_pred)))
print("Accuracy_Score : AdaBoost with SVM =", format(metrics.accuracy_score(y_test, ada_svm_pred)))

Accuracy_Score : Random Forest = 0.7716535433070866
Accuracy_Score : Decision Tree = 0.7007874015748031
Accuracy_Score : XgBoost classifier = 0.7283464566929134
Accuracy_Score : Support Vector Machine (SVM) = 0.7480314960629921
Accuracy_Score : Logistic Regression = 0.7559055118110236
Accuracy_Score : KNN = 0.7165354330708661
Accuracy_Score : AdaBoost = 0.7244094488188977
Accuracy_Score : AdaBoost with SVM = 0.6377952755905512

Feature Importance using Random Forest Classifier

Feature importance is quite necessary as it shows that how much weightage each feature provides in the model building phase.

rfc.feature_importances_

array([0.08219787, 0.24968044, 0.08921725, 0.07090009, 0.07192857,
       0.16683418, 0.13248217, 0.13675942])

pd.Series(rfc.feature_importances_, index=X.columns).plot(kind='barh')

<Axes: >

Here from the above graph, it is clearly visible that Glucose as a feature is the most important in this dataset.

XAI- LIME

!pip install lime

Collecting lime
  Downloading lime-0.2.0.1.tar.gz (275 kB)
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  Preparing metadata (setup.py) ... done
Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from lime) (3.7.1)
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Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.10/dist-packages (from lime) (1.2.2)
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Building wheels for collected packages: lime
  Building wheel for lime (setup.py) ... done
  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283835 sha256=5c9ee3d8cf0e54c92c74ede6dbffce49d57f11adae84dff2afe71942beb21377
  Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89
Successfully built lime
Installing collected packages: lime
Successfully installed lime-0.2.0.1

import lime
from lime import lime_tabular

explainer = lime_tabular.LimeTabularExplainer(
    training_data=np.array(X_train),
    feature_names=X_train.columns,
    class_names=['No diabetes', 'Has diabetes'],
    mode='classification'
)

exp = explainer.explain_instance(
    data_row=X_test.iloc[1],
    predict_fn=rfc.predict_proba
)

exp.show_in_notebook(show_table=True,show_all = False)

exp = explainer.explain_instance(
    data_row=X_test.iloc[10],
    predict_fn=rfc.predict_proba
)

exp.show_in_notebook(show_table=True)

Saving Model – Random Forest

import pickle
saved_model = pickle.dumps(rfc)
rfc_from_pickle = pickle.loads(saved_model)
rfc_from_pickle.predict(X_test)

array([0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0,
       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,
       0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
       1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1,
       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,
       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1,
       1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1])

diabetes_df.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

diabetes_df.tail()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
763	10	101	76	48	180	32.9	0.171	63	0
764	2	122	70	27	0	36.8	0.340	27	0
765	5	121	72	23	112	26.2	0.245	30	0
766	1	126	60	0	0	30.1	0.349	47	1
767	1	93	70	31	0	30.4	0.315	23	0

rfc.predict([[0,137,40,35,168,43.1,2.228,33]]) #4th patient

array([1])

rfc.predict([[10,101,76,48,180,32.9,0.171,63]]) # 763 th patient

array([0])